World's Best Scientists 2026 revealed!
Xavier Lladó

Xavier Lladó

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Computer Science
Spain
2025

D-Index & Metrics

Computer Science

D-Index
47
Citations
10782
World Ranking
6405
National Ranking
82

Research.com Recognitions

  • 2025 - Research.com Computer Science in Spain Leader Award
  • 2022 - Research.com Computer Science in Spain Leader Award

Overview

Xavier Lladó is affiliated with the University of Girona in Spain. Their research primarily focuses on the intersection of medicine and computer science, with significant contributions to the fields of radiology, nuclear medicine and imaging, neurology, computer vision and pattern recognition, epidemiology, and pathology and forensic medicine.

Their work spans several main topics, including:

  • Brain Tumor Detection and Classification
  • Medical Image Segmentation Techniques
  • Acute Ischemic Stroke Management
  • Multiple Sclerosis Research Studies
  • Advanced MRI Techniques and Applications
  • Advanced Neuroimaging Techniques and Applications
  • Intracerebral and Subarachnoid Hemorrhage Research

Frequent co-authors collaborating with Xavier Lladó include Arnau Oliver, Àlex Rovira, Albert Clèrigues, Sergi Valverde, and Deborah Pareto.

Key recent publications illustrate their contributions to neuroimaging and medical image analysis:

  • "Improving the detection of autism spectrum disorder by combining structural and functional MRI information," 2020, NeuroImage Clinical
  • "Deep learning for mass detection in Full Field Digital Mammograms," 2020, Computers in Biology and Medicine
  • "Hemorrhagic stroke lesion segmentation using a 3D U-Net with squeeze-and-excitation blocks," 2021, Computerized Medical Imaging and Graphics
  • "QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results," 2022, The Journal of Machine Learning for Biomedical Imaging
  • "Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI," 2023, NeuroImage Clinical

Their research is published frequently in venues such as NeuroImage Clinical, Computerized Medical Imaging and Graphics, arXiv (Cornell University), Frontiers in Neuroscience, and Computers in Biology and Medicine.

Best Publications

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • A state of the art in structured light patterns for surface profilometry

    Joaquim Salvi;Sergio Fernandez;Tomislav Pribanic;Xavier Llado

  • Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review.

    Jose Bernal;Kaisar Kushibar;Daniel S. Asfaw;Sergi Valverde

  • A review of atlas-based segmentation for magnetic resonance brain images

    Mariano Cabezas;Arnau Oliver;Xavier Lladó;Jordi Freixenet

  • Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach.

    Sergi Valverde;Mariano Cabezas;Eloy Roura;Sandra González-Villà

  • The SLAM problem: a survey

    Josep Aulinas;Yvan Petillot;Joaquim Salvi;Xavier Lladó

  • Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

    Xavier Lladó;Arnau Oliver;Mariano Cabezas;Jordi Freixenet

  • Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

    Hugo J. Kuijf;Adria Casamitjana;D. Louis Collins;Mahsa Dadar

  • Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

    Olivier Commowick;Audrey Istace;Michaël Kain;Baptiste Laurent

  • A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images

    Soumya Ghose;Arnau Oliver;Robert Martí;Xavier Lladó

  • Automatic mass detection in mammograms using deep convolutional neural networks

    Richa Agarwal;Oliver Diaz;Xavier Lladó;Moi Hoon Yap

  • One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

    Sergi Valverde;Mostafa Salem;Mariano Cabezas;Deborah Pareto

  • False positive reduction in mammographic mass detection using local binary patterns

    Arnau Oliver;Xavier Lladó;Jordi Freixenet;Joan Martí

  • A review on brain structures segmentation in magnetic resonance imaging

    Sandra Gonzlez-Vill;Arnau Oliver;Sergi Valverde;Liping Wang

  • Automatic microcalcification and cluster detection for digital and digitised mammograms

    Arnau Oliver;Albert Torrent;Xavier Lladó;Meritxell Tortajada

  • A Qualitative Review on 3D Coarse Registration Methods

    Yago Díez;Ferran Roure;Xavier Lladó;Joaquim Salvi

  • Improving the detection of autism spectrum disorder by combining structural and functional MRI information

    Mladen Rakić;Mariano Cabezas;Kaisar Kushibar;Arnau Oliver

  • Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features.

    Kaisar Kushibar;Sergi Valverde;Sandra González-Villà;Jose Bernal

  • A textural approach for mass false positive reduction in mammography.

    Xavier Lladó;Arnau Oliver;Jordi Freixenet;Robert Marti

  • Deep learning for mass detection in Full Field Digital Mammograms.

    Richa Agarwal;Oliver Díaz;Oliver Díaz;Moi Hoon Yap;Xavier Lladó

  • Non-Rigid Metric Shape and Motion Recovery from Uncalibrated Images Using Priors

    A. Del Bue;X. Llad;L. Agapito

  • Automated detection of multiple sclerosis lesions in serial brain MRI

    Xavier Lladó;Onur Ganiler;Arnau Oliver;Robert Martí

Frequent Co-Authors

Arnau Oliver
Arnau Oliver University of Girona
Jordi Freixenet
Jordi Freixenet University of Girona
Joaquim Salvi
Joaquim Salvi University of Girona
Fabrice Meriaudeau
Fabrice Meriaudeau University of Franche-Comté
Yvan Petillot
Yvan Petillot Heriot-Watt University
Lourdes Agapito
Lourdes Agapito University College London
Sebastien Ourselin
Sebastien Ourselin King's College London
Maria Petrou
Maria Petrou Imperial College London
Bennett A. Landman
Bennett A. Landman Vanderbilt University
Marc Modat
Marc Modat King's College London

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